دانلود کتاب Machine Learning and Data Mining for Sports Analytics. 9th International Workshop, MLSA 2022 Grenoble, France, September 19, 2022 Revised Selected Papers

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Preface
Organization
Contents
Football
Towards Expected Counter - Using Comprehensible Features to Predict Counterattacks
1 Introduction
2 Framework for Understanding Complex Sequences
3 Definition of Sequences of Interest and Success Criteria
3.1 Rule-based Identification of Persistent Open-Play Turnovers
3.2 Definition of Success Criteria for Counterattacks
3.3 Emerging Dataset
4 Comprehensible Features for Prediction
4.1 Constructing Features from Domain-Specific Assumptions
4.2 Influence of Ball Loss Location for Feature Assessment
4.3 Prediction Capability of the Constructed Features
5 Model-based Test of Features
6 Conclusion
References
Shot Analysis in Different Levels of German Football Using Expected Goals
1 Introduction
2 Related Work
3 Methodology
3.1 Data
3.2 Statistical Analysis
3.3 Expected Goals Models
4 Results
4.1 Statistical Analysis
4.2 Expected Goals Models
5 Conclusions
A Box plots of significantly different distributions
References
Analyzing Passing Sequences for the Prediction of Goal-Scoring Opportunities
1 Introduction
2 Problem Definition
3 Methodology
3.1 Tracking Data
3.2 Event Data
3.3 Data Alignment
3.4 Extraction of Goal Scoring Opportunities
3.5 Pitch Partitioning
3.6 Sequential Pattern Mining
4 Experimental Study
5 Style of Play for the Top-2 Teams
6 Conclusions and Future Work
References
Let\'s Penetrate the Defense: A Machine Learning Model for Prediction and Valuation of Penetrative Passes
1 Introduction
2 Related Work
3 Penetrative Pass Prediction and Valuation
3.1 Dataset and Preprocessing
3.2 Potential Penetrative Pass Situation
3.3 Penetrative Pass Label Generation
3.4 Penetrative Pass Decomposed Model
4 Experiments and Results
4.1 Best Performing Prediction Model
4.2 Does a Penetrative Pass Affect Goal Scoring or Conceding?
4.3 Teams\' Penetrative Performance Analysis
4.4 Field Section Analysis:
5 Conclusion
References
Evaluation of Creating Scoring Opportunities for Teammates in Soccer via Trajectory Prediction*-12pt
1 Introduction
2 Proposed Framework
2.1 Potential Score Model in Modified OBSO
2.2 C-OBSO with Trajectory Prediction
3 Experiments
3.1 Dataset
3.2 Data Processing for Verification
3.3 Our Model Verification
3.4 C-OBSO Results
4 Related Work
5 Conclusion
A Overview of our Method
B Off-Ball Scoring Opportunity ch5Spearman18
C Variational Recurrent Neural Network ch5Chung15
D Graph Variational Recurrent Neural Network ch5Yeh2019
E Validation Results of Trajectory Prediction Model
F C-OBSO and OBSO Results Without the Potential Score Model
G Relationship Between Rating, C-OBSO, and Goal
References
Cost-Efficient and Bias-Robust Sports Player Tracking by Integrating GPS and Video
1 Introduction
2 Related Work
2.1 Optical Player Tracking
2.2 GPS-Based Player Tracking
2.3 GPS-OTS Integration Approach
3 Main Contributions
3.1 Anchor Starter Detection
3.2 Anchor Segment Detection
3.3 GPS-OTS Trajectory Matching per Anchor Segment
3.4 Initial Estimation of GPS Biases
3.5 Fine-Tuning GPS Biases
4 Experiments
4.1 Data Preparation
4.2 Implementation Detail
4.3 Model Evaluation
5 Conclusion and Future Work
References
Racket Sports
Predicting Tennis Serve Directions with Machine Learning
1 Introduction
2 Related Work
3 Basic Information About Tennis Serves
4 Data
5 Feature Engineering
5.1 Outcome of Previous Points
5.2 Fatigue
5.3 Performance Anxiety
5.4 Other Features
6 Machine Learning
7 Discussion
8 Conclusion and Future Work
References
Discovering and Visualizing Tactics in a Table Tennis Game Based on Subgroup Discovery
1 Introduction
2 Methodology
2.1 Dataset
2.2 Tactics in Table Tennis
2.3 Mining Frequent and Discriminant Sequential Pattern
2.4 Summary of Assumptions
3 Results
3.1 Presentation of the Obtained Alternate Sequences
3.2 Visualization of the Tactics
4 Conclusion and Perspectives
A Appendix
References
Cycling
Athlete Monitoring in Professional Road Cycling Using Similarity Search on Time Series Data
1 Introduction
2 Related Work
3 Materials
3.1 Materials
3.2 Data Preprocessing
4 Methodology
4.1 Selection of Potential Matches
4.2 Taylor-made Approach
4.3 Dimensionality Reduction Approach
5 Results
5.1 Modeling Performance
5.2 Athlete Monitoring
6 Discussion
7 Conclusion
References
Author Index